Temporal-based professional similarity

Patent metadata

Field	Value
Publication number	US-10042894-B2
Application number	US-201414528643-A
Country	US
Kind code	B2
Filing date	Oct 30, 2014
Priority date	Oct 31, 2013
Publication date	Aug 7, 2018
Grant date	Aug 7, 2018

Abstract

A system and method for temporal-based professional similarity are provided. In example embodiments, a request to identify, from among a plurality of member profiles of a social network service, a profile that is similar to a source profile, is received. Profile data of the source profile and a candidate profile are accessed from the social network service. Profile features are extracted from the profile data. The profile features include source features extracted from the profile data of the source profile and candidate features extracted from the profile data of the candidate profile. Respective profile features correspond to temporal data included in the profile data. Data structures are generated by structuring the profile features according to the temporal data. The data structures include a source data structure generated using the source features and a candidate data structure generated using the candidate features. A profile similarity score is determined by comparing the candidate data structure with the source data structure. The profile similarity score indicates the similarity between the candidate profile and the source profile.

First claim

What is claimed is:

1. A method comprising:
receiving, by a computer system comprising a hardware processor of a machine, a request to identify a similar profile, from among a plurality of member profiles of members of a social network service, that is similar to a source profile of a member of the social network service, the request comprising an identifier that identifies the source profile;
accessing, by the computer system, profile data from the social network service, the profile data including profile data of the source profile and profile data of a candidate profile from the plurality of member profiles;
extracting, by the computer system, profile features from the profile data, the profile features including source features extracted from the profile data of the source profile and candidate features extracted from the profile data of the candidate profile, respective profile features corresponding to temporal data included in the profile data, at least a portion of the temporal data indicating that each profile feature corresponding to the at least a portion of the temporal data previously applied to the member to which the profile feature corresponds, but that the profile feature corresponding to the portion of the temporal data no longer applies to the member to which the profile feature corresponds;
generating, by the computer system, data structures by structuring the profile features according to the temporal data, the data structures including a source data structure generated using the source features and a candidate data structure generated using the candidate features;
calculating, by the computer system, a profile similarity score by comparing the candidate data structure with the source data structure, the profile similarity score indicating a similarity between the candidate profile and the source profile;
determining the similar profile comprises the candidate profile based on the profile similarity score; and
causing presentation of the similar profile to a requester that initiated the request.

2. The method of claim 1 , wherein each of the data structures comprises a node sequence that includes a plurality of nodes, the node sequence generated by:
generating the plurality of nodes using profile features of a particular member profile, wherein respective nodes of the plurality of nodes include at least one of the profile features of the particular member profile;
ordering the plurality of nodes sequentially according to the temporal data corresponding to the profile features included in the respective nodes of the plurality of nodes; and
assembling the node sequence with the ordered plurality of nodes.

3. The method of claim 2 , wherein the generating the plurality of nodes further comprises:
identifying an employment position from the particular member profile;
associating a particular node of the plurality of nodes with the identified employment position; and
including the profile features of the particular member profile corresponding to the identified employment position in the particular node.

4. The method of claim 2 , wherein the calculating the profile similarity score further comprises:
assembling aligned node pairs by aligning the candidate data structure with the source data structure, respective aligned node pairs including a node from the candidate data structure and a node from the source data structure;
calculating a node similarity score for the respective aligned node pairs by comparing profile features of the node from the candidate data structure and profile features of the node from the source data structure;
combining the node similarity scores for the respective aligned node pairs to determine an alignment score; and
calculating the profile similarity score, in part, using the alignment score.

5. The method of claim 4 , further comprising:
iterating through at least one alignment of possible alignments between the candidate data structure and the source data structure;
calculating the alignment score for respective alignments of the possible alignments;
identifying an optimal alignment score based on the alignment score for the respective alignments of the possible alignments; and
calculating the profile similarity score, in part, using the optimal alignment score.

6. The method of claim 4 , further comprising:
deriving employment transition data based on profile features of a particular node sequence;
including the derived employment transition data in respective nodes of the particular node sequence according to the profile features of the particular node sequence used to derive the employment transition data; and
calculating the node similarity score, in part, using the derived employment transition data.

7. The method of claim 4 , further comprising:
deriving a duration feature and a recentness feature using the temporal data;
determining a weighting factor based on at least one of the derived duration feature and the derived recentness feature; and
calculating the node similarity score, in part, using the weighting factor.

8. The method of claim 7 , wherein the weighting factor comprises terms including at least one of:
a duration difference term based on a difference between the derived duration feature of the node from the candidate data structure and the node from the source data structure;
a recentness difference term based on a difference between the derived recentness feature of the node from the candidate data structure and the node from the source data structure;
a duration sum term based on a sum of the derived duration feature of the node from the candidate data structure and the node from the source data structure; and
a recentness sum based on a sum between the derived recentness feature of the node from the candidate data structure and the node from the source data structure.

9. The method of claim 2 , further comprising:
identifying similar nodes among the plurality of nodes included in the node sequence by comparing the profile features included in the respective nodes of the plurality of nodes;
determining a consecutive relationship between two identified similar nodes; and
merging the identified similar nodes with the consecutive relationship in the node sequence.

10. The method of claim 4 , wherein the calculating the node similarity score in calculating the node similarity score for a predetermined number of the aligned node pairs.

11. The method of claim 4 , further comprises:
skipping the calculating the node similarity score based on an analysis of the profile features included in the aligned node pair; and
applying a gap penalty to the alignment score corresponding to the skipping.

12. The method of claim 4 , wherein the calculating the node similarity score includes using a prediction model, the prediction model being any one of a logistic regression model, a Naive Bayes model, a support vector machines (SVM) model, a decision trees model, and a neural network model.

13. The method of claim 12 , wherein the prediction model uses training data including positive training data and negative training data, wherein the training data is based on a profile search, performed by a user, for member profiles similar to another profile, the profile search providing member profile search results to the user,
wherein the positive training data is based on contacted member profiles included in results of the member profile search, the contacted member profiles being contacted by the user, and
wherein the negative training data being based on non-contacted member profiles included in the member profile search results; the non-contacted member profi

Assignees

• Microsoft Technology Licensing Llc

Inventors

• Xu Ye
• Li Zang
• Gupta Abhishek
• Bugdayci Ahmet
• Bhasin Anmol

Classifications

• G06Q10/40

  Business processes related to social networking or social networking services · CPC title

• G06F16/24578 Primary

  using ranking · CPC title

• G06Q50/01

  Physics · mapped topic

• G06F17/3053 Primary

  Physics · mapped topic

• G06Q10/42

  Determination of affinities or common interests between users · CPC title